Machine learning has been very successful, but its successes rely on human machine learning experts to define the learning problem, select, collect and preprocess the training data, choose appropriate ML architectures (deep learning, random forests, SVMs, ...) and their hyperparameters, and finally evaluate the suitability of the learned models for deployment. As the complexity of these tasks is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that are more bullet-proof and can be used easily without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.

We welcome original submissions up to 6 pages in JMLR Workshop and Proceedings format (not including references). In addition, we encourage submissions of previously-published material (clearly marked as such) that is closely related to the workshop topic.

We especially encourage demos of working AutoML systems. Demos will be presented during the workshop as a short spotlight demo video and a live demonstration during the poster session. You can submit a demo proposal by submitting an accompanying paper describing the demo and a uploading a draft of your demo video. If accepted, you may of course change either before the workshop.

Accepted original papers will be made available online and will be presented as posters and poster spotlight presentations at the workshop. The best 2-3 papers will be invited for oral plenary presentation. All other accepted papers will be presented as posters and short poster spotlight presentations.